An improved ORB-GMS image feature extraction and matching algorithm*

Abstract

Feature point extraction and matching is the key technology in object detection and simultaneous localization and mapping (SLAM). Aiming at the problems such as easy redundancy of feature points extracted by traditional ORB algorithm, low matching accuracy of mainstream robust estimation algorithms and low real-time performance, an improved ORB-GMS image feature extraction and matching algorithm is proposed. Firstly, the algorithm uses the gray value of the image to calculate the adaptive extraction threshold of the feature points. Then the image pyramid is constructed according to the image size. The set number of total feature points to be extracted is evenly distributed to each layer image according to the area ratio; Extract feature points from each layer of the image pyramid, and count the extracted feature points from each layer. If the number of feature points extracted from each layer meets the set number of images from each layer, the extraction ends. Then the quadtree algorithm is used to homogenize the feature points. Finally, the network scoring model is optimized from 8 neighborhood to 4 neighborhood, which reduces the computing time. Experimental results show that the matching accuracy of the proposed algorithm is 14% higher than that of the original algorithm, and the running time is 12% lower.